Since its inception in 2016, federated learning has evolved into a highly promising decentral-
ized machine learning approach, facilitating collaborative model training across numerous …

As the location-based service (LBS) plays an increasingly important role in real life, the topic
of positioning attracts more and more attention. Under different environments and principles …

The advent of the Artificial Intelligent Internet of Things (AIoT) has sparked a revolution in the
deployment of intelligent systems, driving the need for innovative data processing …

Wi-Fi positioning has drawn great attention in the field of indoor positioning, due to its low
cost, easy deployment, and large positioning range. However, the Wi-Fi signal is highly …

Federated learning (FL) is a distributed learning paradigm that maximizes the potential of
data-driven models for edge devices without sharing their raw data. However, devices often …

Federated learning (FL) is an emerging paradigm for decentralized training of machine
learning models on distributed clients, without revealing the data to the central server. The …

Indoor localization (IL) is a significant topic of study with several practical applications,
particularly in the context of the Internet of Things (IoT) and smart cities. The area of IL has …

The increasing popularity of wearable-based Collaborative Indoor Positioning Systems
(CIPSs) has led to the development of new methods for improving positioning accuracy …

In this article, the use of channel state information (CSI) for indoor positioning is studied. In
the considered model, a server equipped with several antennas sends pilot signals to users …

With the widespread use of machine learning, especially deep learning, in high-mobility
vehicular networks, vehicle-to-everything (V2X) communication has received lots of attention …